Communication throughput prediction devices that predict communication throughput, which is a size of data (amount of data) that are transferred (transmitted) by way of a communication network per unit time, have been used.
Communication throughput fluctuates due to various causes. For example, the communication throughput of a best-effort network, such as the Internet or a mobile network, changes moment by moment because of complicated interaction from various causes, such as influence from cross traffic and changes in the radio wave condition.
On the other hand, communication throughput substantially influences the quality of services provided in cloud services and the like, which are provided on best-effort networks. Thus, for telecommunication carriers, predicting communication throughput and fluctuation therein with high accuracy is one of the important challenges.
With regard to technologies for predicting communication throughput, in PTL 1, for example, a method for discerning whether the state of communication throughput is in a stable state (stable state) or an unstable state (non-stable state) is described. Specifically, a communication throughput prediction device described in PTL 1 performs stationarity discernment using a unit root test on time series data of communication throughput to discern whether the state of communication throughput is in a stable state or a non-stable state. On the basis of the obtained stationarity discernment result for the state of communication throughput, the communication throughput prediction device identifies a prediction model of communication throughput.
In NPL 1, for example, a method for calculating the mixing ratio included in the state of communication throughput, which is based on the assumption that the state of communication throughput is not simply categorized into either a steady state or a non-steady state, and in actuality, is brought to a state into which a steady state and a non-steady state are mixed at a certain proportion (referred to as a mixing ratio), is described. Specifically, a communication throughput prediction device described in NPL 1 employs, as a mixing ratio, an exponentially smoothed moving average (that is, a real value not less than 0 and not greater than 1), which is obtained by applying a smoothing filter to discernment result time series data in which stationarity discernment results for the state of communication throughput are arranged in a time series. On the basis of the mixing ratio obtained in such a way, the communication throughput prediction device mixes a steady model and a non-steady model to construct a prediction model of communication throughput.